In the state of the art, such segmenting of non-planar surfaces is possible only manually. For example, in the System Philips QLab®, the user of the software must draw in the cusp contours of the valvular cusp of the mitral valve in 9-12 parallel sectional images, which are available in 3D/4D-ultrasound volume data, or can be generated from such data. These manually drawn cusp contours then serve to depict a three-dimensional contour in a three-dimensional view of the respective heart valve.
The manual segmentation of such non-planar surfaces by the user is cumbersome and takes a long time. In addition, it cannot be excluded that the user might detect incorrect contours on the two-dimensional sectional images, which are possibly generated from a three-dimensional image data set (e.g. Ultrasound, CT (computed tomography) or MR (magnetic resonance)), so that the manually detected surfaces may possibly contain errors. In addition, valvular cusps may show up twice on four-dimensional image data sets, i.e. three-dimensional image data sets acquired along a time line. This will happen, if the time resolution during the acquisition is not sufficient to capture a fast cusp movement. When such data sets are processed slice by slice, i.e. on the basis of two-dimensional images, the spatial context gets lost and the contouring of the valvular cusp is inconsistent
Finally, the manual segmenting depends strongly on the respective user, so that different results may be expected from the same three- or four-dimensional image data sets, depending on which user has been processing the data sets.